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On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
March 5, 2024, 2:45 p.m. | Boyao Li, Alexandar J. Thomson, Matthew M. Engelhard, David Page
cs.LG updates on arXiv.org arxiv.org
Abstract: Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs). In this paper, we propose an innovative solution by constructing infinite tree-structured PGMs that correspond exactly to neural networks. Our research reveals that DNNs, during forward propagation, indeed perform approximations of PGM inference that are precise in this alternative PGM structure. Not only does our research complement existing studies that describe neural networks as kernel machines or infinite-sized …
abstract arxiv cs.lg indeed interpretation networks neural networks paper propagation research semantics solution stat.ml tree type
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